Construction of surface boundary conditions for regional climate modeling in China by using the remote sensing data

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1 Copyright 2005 Society of Photo-Optical Instrumentation Engineers Construction of surface boundary conditions for regional climate modeling in China by using the remote sensing data Wei Gao a,d, Zhiqiang Gao a.b.d, Hyun I. Choi c Min Xu c, and James Slusser a a USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO; b Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; c Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA d International Center for Desert Affairs-Research for Sustainable Development in Arid and Semi-arid Land, Urumqi, China 1. INTRODUCTION The continuing rise in atmospheric CO 2 is considered as a main cause of the future changes in global climate 1,2,3. Predicted climate changes include an increase in mean annual air temperature and alterations in precipitation pattern and cloud cover. Elevated atmospheric CO 2 and climate changes are expected to influence the ecosystems 4. The regional climate models (RCMs) will likely remain primary tools for climate prediction in the foreseeable future. The importance of RCMs is increasing in addressing scientific problems associated with climate variability, changes, and impacts at regional scales 5. The RCMs have been also used in climate impact studies on ecosystems, especially in agricultural crops by generating climate scenarios for input to crop models. With a large volume of satellite remote sensing data of the earth terrestrial surface becoming available, precisely monitoring the dynamics of the land surface state variables for agricultural and land use management becomes possible 6. With the effort to study the climate crop interactions we plan to use a CWRF model 7,8,9, (a climate extension of the Weather Research and Forecasting model-wrf) developed by the Illinois State Water Survey to form the climate scenarios. The WRF model 10 is based upon the most advanced supercomputing technologies and promises greater efficiency in computation and flexibility in new module incorporation. This extension inclusively incorporates all WRF functionalities for numerical weather predictions while enhancing the capability for climate applications. To represent the surface-atmosphere interactions the CWRF requires specification of surface boundary conditions (SBCs) over both land and oceans. A comprehensive set of SBCs based on best observational data is desired for CWRF general applications for all effective, dynamically coupled or uncoupled, combinations of the surface modules, as well as for any specific region of the world 11,12. This report followed the approach of Liang et al. 12 presents a preliminary work to construct vegetative SBCs for the CWRF modeling effort in China domain by using remote sensing data from TM, AVHRR, MODIS which are freely available. The full list of the CWRF SBCs was defined by Liang STUDY AREA The China domain is centered at (30ºN, 110ºW) using the Lambert Conformal Conic map projection and 30-km horizontal grid spacing with total grid points of 301 (west-east) x 251 (south-north). The domain covers the whole China land and near countries which represents the regional climate that results from interactions between the planetary circulation (as forced by the lateral boundary conditions (LBCs)) and China surface processes, including geography, vegetation, soil and coastal oceans. 3. CONSTRUCTION of the VEGETATIVE CWRF SBCs in CHINA A critical requirement in constructing the SBCs for CWRF use is that each variable must be defined globally with no missing values and with physical consistency across all relevant parameters. Missing data, if any, must be appropriately filled. For mesoscale weather and climate modeling, the raw data should be available at the finest possible resolution to facilitate a more realistic representation of surface heterogeneity effects 12. Existing observational databases have various resolutions finer or coarser than the grid of the CWRF, a wide range of map projections and data formats, and often contain missing values or inconsistencies between variables. It is necessary to process the data onto the CWRF-specific grid mesh and input data format. Horizontal data remapping uses Geographic Information System (GIS) software application tools, Arc/Info and Arc/Map, from Environmental Systems Research Institute, Inc. In particular, the GIS tools are used to determine the Address correspondence to wgao@uvb.nrel.colostate.edu, phone ; fax Remote Sensing and Modeling of Ecosystems for Sustainability II, edited by Wei Gao, David R. Shaw, Proc. of SPIE Vol (SPIE, Bellingham, WA, 2005) X/05/$15 doi: / Proc. of SPIE

2 geographic conversion information from a specific map projection of raw data to the identical CWRF grid system. The information includes location indices, geometric distances, or fractional areas of all input cells contributing the each CWRF grid. It then can be applied to remap all variables of the same projection. Remapping is completed by a bilinear interpolation method in terms of the geometric distances if the raw data resolution is low or otherwise a mass conservative approach as weighted by the fractional areas. For convenience, the geographic location of a point is hereafter referred as a pixel for raw data and a grid for CWRF result. With the remote sensing data availability we have constructed vegetative CWRF SBCs in China domain for future CWRF modeling effort on climate-crop interactions. These vegetative SBCs include Surface Characteristic Identification (SCI), Land Cover Category (LCC), Fractional Vegetation Cover (FVC), and Leaf Area Index (LAI). 3.1 Surface Characteristic Indentification (SCI) The CWRF incorporates the SCI to distinguish broad surface categories that invoke distinct surface modules. The SCI consists of eight categories: urban and built-up, soil, wetland, glacier, shallow lake, deep lake, sea ice, and ocean. Figure 1 describes the SCI distribution over the CWRF China domain. The SCI identifies these different categories. Fig. 1. The geographic distribution of SCI over the CWRF China domain 3.2 Land Cover Category(LCC) The CWRF adopts the U.S. Geological Survey (USGS) land cover classification, which consists of 24 categories. The USGS land cover data were developed using the AVHRR satellite-derived NDVI composites from April 1992 through March The raw data are available at 1-km spacing on the geographic coordinate system in the BIL image format ( globe_int.html), converted into the ERDAS IMG format, then to ArcGIS raster grid and polygon coverage, and remapped onto the CWRF projection. Fig. 2 demonstrates the LCC geographic distribution over the CWRF China domain. 3.3 Fractional Vegetation Cover (FVC) The FVC is one ecological parameter that determines the contribution partitioning between bare soil and vegetation for surface evapotranspiration, photosynthesis, albedo, and other fluxes crucial to land-atmosphere interactions as it is described by Liang 12. It is derived from the same global 1-km AVHRR satellite product as for LCC. The 10-day composites from April Proc. of SPIE

3 1992 to March 1993 were used to determine the annual maximum NDVI ( N p, max ) for each land cover category, minimizing the effect of cloud contamination on data quality. The final FVC calculated by the method of Liang 12 is obtained by the areaweighted averaging of the vegetation cover for all pixels within each CWRF grid. Fig. 3 illustrates the FVC geographic distributions over the CWRF China domain. Fig. 2. The geographic distribution of LCC over the CWRF China domain Fig. 3 The geographic distribution of FVC over the CWRF China domain 3.4 Leaf Area Index(LAI) The LAI is defined as the total one-sided area of all green canopy elements plus dead leaves over vegetated ground area. It is constructed from the global monthly mean distributions of green vegetation leaf area index data during July 1981-December 1999 AVHRR data at 8-km spacing on the Interrupted Goode Homolosine projection 13,14. Detailed method and comparison Proc. of SPIE

4 on obtaining the FVC can be found in Liang s discussion 12. Fig. 4 demonstrates the LAI geographic distribution over the CWRF China domain. Fig. 4 The geographic distribution of LAI over the CWRF China domain 4. CONCLUSIONS As an initial effort to use CWRF model to study climate-crop interaction in China domain this report focuses on the construction of vegetative SBCs by using remote sensing data from TM, AVHRR, MODIS which are freely available. The SBC s were constructed onto the 30 km CWRF grid resolution in this effort. Other SBC needed in CWRF China domain and the implementation of SBC for general CWRF application will be constructed and performed in the future work. The uncertainties of the SBCs will be evaluated in the future as well. ACKNOWLEDGEMENTS This work was supported by National Natural Science Foundation of China (Project: and ), National 973 Key Project of China (2002CB and G ), Outstanding Overseas Chinese Scholars Fund of Chinese Academy of Sciences ( ). Chuihui Plan of Ministry of Education of China (Z ), and the USDA/CSREES (Agreement ). REFERENCES 1. J.M. Melillo, A.D. McGuire, D.W. Kicklighter, B.M. III, C.J. Vorosmarty, and A.L. Schloss, Global climate change and terrestrial net primary production, Nature, 363, pp , M.K. Cao, and F.I. Woodward, Dynamic responses of terrestrial ecosystem carbon cycling to global climate change, Nature, 393, pp , D. McGuire, and J.M. Melillo, Equilibrium responses of soil carbon to climate change: empirical and process-based estimates, Journal of Biogeography, 22, pp , M. Riedo, D. Gyalistras, and J. Fuhrer, Net primary production and carbon stocks in differently managed grasslands: simulation of site-specific sensitivity to an increase in atmospheric CO and to climate change, Ecological Modelling, pp , F. Giorgi, and L.O. Mearns, Introduction to special section: Regional climate modeling revisited, J. Geophys. Res. 104, pp , Proc. of SPIE

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